





import argparse
from collections import defaultdict

import yaml


lowerCAmelCase_        =							'''docs/source/en/_toctree.yml'''






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Optional[Any]:






				"""simple docstring"""


				snake_case_		:       Optional[int]           =    defaultdict(_UpperCamelCase					)
				for doc in model_doc:
								counts[doc["local"]] += 1
				snake_case_		:       List[str]           =    [key for key, value in counts.items() if value > 1]

				snake_case_		:       List[str]           =    []
				for duplicate_key in duplicates:
								snake_case_		:       Union[str, Any]           =    list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}					)
								if len(_UpperCamelCase					) > 1:
												raise ValueError(
												    f'''{duplicate_key} is present several times in the documentation table of content at '''
												    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
												    '''others.'''					)
								# Only add this once
								new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]					)

				# Sort
				return sorted(_UpperCamelCase					,							key=lambda _UpperCamelCase					: s["title"].lower()					)






def 	lowerCamelCase_		(							_UpperCamelCase=False					)						->		Dict:






				"""simple docstring"""


				with open(_UpperCamelCase					,							encoding='''utf-8'''					) as f:
								snake_case_		:       List[Any]           =    yaml.safe_load(f.read()					)

				# Get to the API doc
				snake_case_		:       Tuple           =    0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				snake_case_		:       List[Any]           =    content[api_idx]['''sections''']

				# Then to the model doc
				snake_case_		:       List[Any]           =    0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				snake_case_		:       Any           =    api_doc[model_idx]['''sections''']

				snake_case_		:       Any           =    [(idx, section) for idx, section in enumerate(_UpperCamelCase					) if '''sections''' in section]
				snake_case_		:       Optional[int]           =    False
				for idx, modality_doc in modalities_docs:
								snake_case_		:       Dict           =    modality_doc['''sections''']
								snake_case_		:       int           =    clean_model_doc_toc(_UpperCamelCase					)

								if old_modality_doc != new_modality_doc:
												snake_case_		:       Optional[int]           =    True
												if overwrite:
																snake_case_		:       Dict           =    new_modality_doc

				if diff:
								if overwrite:
												snake_case_		:       str           =    model_doc
												snake_case_		:       int           =    api_doc
												with open(_UpperCamelCase					,							'''w'''					,							encoding='''utf-8'''					) as f:
																f.write(yaml.dump(_UpperCamelCase					,							allow_unicode=_UpperCamelCase					)					)
								else:
												raise ValueError(
												    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
	lowerCAmelCase_        =							argparse.ArgumentParser()
	parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
	lowerCAmelCase_        =							parser.parse_args()

	check_model_doc(args.fix_and_overwrite)

import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


A						=			{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 1_3_1_0_7_2,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
}


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Union[str, Any]							)   ->    Optional[Any]:
		return torch.atana(UpperCamelCase			,       UpperCamelCase							) / math.pi * 2


def 	lowerCamelCase					(	UpperCamelCase     :				str							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.sin(t * math.pi / 2							) ** 2
		_lowerCamelCase				   =  (1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(UpperCamelCase			,       UpperCamelCase							)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		pass





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     Any		)      ->	Optional[Any]:
				super().__init__()

				_lowerCamelCase				   =  DiffusionAttnUnetaD(snake_case__     ,  n_attn_layers=4		)
				_lowerCamelCase				   =  deepcopy(self.diffusion		)
				_lowerCamelCase				   =  torch.quasirandom.SobolEngine(1     ,  scramble=snake_case__		)



def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    List[str]:
		_lowerCamelCase				   =  MODELS_MAP[model_name]['url']
		os.system(F"""wget {url} ./"""							)

		return F"""./{model_name}.ckpt"""


A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
A						=			{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
A						=			{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

A						=			{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

A						=			{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    int:
		if name.startswith('skip'							):
				return name.replace('skip'			,       RES_CONV_MAP['skip']							)

		# name has to be of format main.{digit}
		if not name.startswith('main.'							):
				raise ValueError(F"""ResConvBlock error with {name}"""							)

		return name.replace(name[:6]			,       RES_CONV_MAP[name[:6]]							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    Tuple:
		for key, value in ATTN_MAP.items():
				if name.startswith(UpperCamelCase							) and not isinstance(UpperCamelCase			,       UpperCamelCase							):
						return name.replace(UpperCamelCase			,       UpperCamelCase							)
				elif name.startswith(UpperCamelCase							):
						return [name.replace(UpperCamelCase			,       UpperCamelCase							) for v in value]
		raise ValueError(F"""Attn error with {name}"""							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any			,       UpperCamelCase     :				int=13							)   ->    Optional[int]:
		_lowerCamelCase				   =  input_string

		if string.split('.'							)[0] == "timestep_embed":
				return string.replace('timestep_embed'			,       'time_proj'							)

		_lowerCamelCase				   =  0
		if string.startswith('net.3.'							):
				depth += 1
				_lowerCamelCase				   =  string[6:]
		elif string.startswith('net.'							):
				_lowerCamelCase				   =  string[4:]

		while string.startswith('main.7.'							):
				depth += 1
				_lowerCamelCase				   =  string[7:]

		if string.startswith('main.'							):
				_lowerCamelCase				   =  string[5:]

		# mid block
		if string[:2].isdigit():
				_lowerCamelCase				   =  string[:2]
				_lowerCamelCase				   =  string[2:]
		else:
				_lowerCamelCase				   =  string[0]
				_lowerCamelCase				   =  string[1:]

		if depth == max_depth:
				_lowerCamelCase				   =  MID_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  'mid_block'
		elif depth > 0 and int(UpperCamelCase							) < 7:
				_lowerCamelCase				   =  DOWN_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""down_blocks.{depth}"""
		elif depth > 0 and int(UpperCamelCase							) > 7:
				_lowerCamelCase				   =  UP_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - depth - 1}"""
		elif depth == 0:
				_lowerCamelCase				   =  DEPTH_0_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - 1}""" if int(UpperCamelCase							) > 3 else 'down_blocks.0'

		if not string_left.startswith('.'							):
				raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}."""							)

		_lowerCamelCase				   =  string_left[1:]

		if "resnets" in new_layer:
				_lowerCamelCase				   =  convert_resconv_naming(UpperCamelCase							)
		elif "attentions" in new_layer:
				_lowerCamelCase				   =  convert_attn_naming(UpperCamelCase							)
				_lowerCamelCase				   =  new_string_left

		if not isinstance(UpperCamelCase			,       UpperCamelCase							):
				_lowerCamelCase				   =  prefix + '.' + new_layer + '.' + string_left
		else:
				_lowerCamelCase				   =  [prefix + '.' + new_layer + '.' + s for s in string_left]
		return new_string


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    int:
		_lowerCamelCase				   =  {}
		for k, v in state_dict.items():
				if k.endswith('kernel'							):
						# up- and downsample layers, don't have trainable weights
						continue

				_lowerCamelCase				   =  rename(UpperCamelCase							)

				# check if we need to transform from Conv => Linear for attention
				if isinstance(UpperCamelCase			,       UpperCamelCase							):
						_lowerCamelCase				   =  transform_conv_attns(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
				else:
						_lowerCamelCase				   =  v

		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Dict							)   ->    Optional[Any]:
		if len(UpperCamelCase							) == 1:
				if len(v.shape							) == 3:
						# weight
						_lowerCamelCase				   =  v[:, :, 0]
				else:
						# bias
						_lowerCamelCase				   =  v
		else:
				# qkv matrices
				_lowerCamelCase				   =  v.shape[0]
				_lowerCamelCase				   =  trippled_shape // 3
				for i in range(3							):
						if len(v.shape							) == 3:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Optional[Any]:
		_lowerCamelCase				   =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

		_lowerCamelCase				   =  args.model_path.split('/'							)[-1].split('.'							)[0]
		if not os.path.isfile(args.model_path							):
				assert (
				    model_name == args.model_path
				), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
				_lowerCamelCase				   =  download(UpperCamelCase							)

		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_rate']
		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_size']

		_lowerCamelCase				   =  Object()
		_lowerCamelCase				   =  sample_size
		_lowerCamelCase				   =  sample_rate
		_lowerCamelCase				   =  0

		_lowerCamelCase				   =  UNetaDModel(sample_size=UpperCamelCase			,       sample_rate=UpperCamelCase							)
		_lowerCamelCase				   =  diffusers_model.state_dict()

		_lowerCamelCase				   =  DiffusionUncond(UpperCamelCase							)
		orig_model.load_state_dict(torch.load(args.model_path			,       map_location=UpperCamelCase							)['state_dict']							)
		_lowerCamelCase				   =  orig_model.diffusion_ema.eval()
		_lowerCamelCase				   =  orig_model.state_dict()
		_lowerCamelCase				   =  rename_orig_weights(UpperCamelCase							)

		_lowerCamelCase				   =  set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
		_lowerCamelCase				   =  set(diffusers_state_dict.keys()							) - set(renamed_state_dict.keys()							)

		assert len(UpperCamelCase							) == 0, F"""Problem with {renamed_minus_diffusers}"""
		assert all(k.endswith('kernel'							) for k in list(UpperCamelCase							)							), F"""Problem with {diffusers_minus_renamed}"""

		for key, value in renamed_state_dict.items():
				assert (
				    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
				), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
				if key == "time_proj.weight":
						_lowerCamelCase				   =  value.squeeze()

				_lowerCamelCase				   =  value

		diffusers_model.load_state_dict(UpperCamelCase							)

		_lowerCamelCase				   =  1_00
		_lowerCamelCase				   =  33

		_lowerCamelCase				   =  IPNDMScheduler(num_train_timesteps=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(UpperCamelCase							)
		_lowerCamelCase				   =  torch.randn([1, 2, config.sample_size]			,       generator=UpperCamelCase							).to(UpperCamelCase							)

		_lowerCamelCase				   =  torch.linspace(1			,       0			,       steps + 1			,       device=UpperCamelCase							)[:-1]
		_lowerCamelCase				   =  get_crash_schedule(UpperCamelCase							)

		_lowerCamelCase				   =  DanceDiffusionPipeline(unet=UpperCamelCase			,       scheduler=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(33							)
		_lowerCamelCase				   =  pipe(num_inference_steps=UpperCamelCase			,       generator=UpperCamelCase							).audios

		_lowerCamelCase				   =  sampling.iplms_sample(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       {}							)
		_lowerCamelCase				   =  generated.clamp(-1			,       1							)

		_lowerCamelCase				   =  (generated - audio).abs().sum()
		_lowerCamelCase				   =  (generated - audio).abs().max()

		if args.save:
				pipe.save_pretrained(args.checkpoint_path							)

		print('Diff sum'			,       UpperCamelCase							)
		print('Diff max'			,       UpperCamelCase							)

		assert diff_max < 1e-3, F"""Diff max: {diff_max} is too much :-/"""

		print(F"""Conversion for {model_name} successful!"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()

				parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
				parser.add_argument(
				    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
				)
				parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
				A						=			parser.parse_args()

				main(args)